City University of New York (CUNY) CUNY Academic Works All Dissertations, Theses, and Capstone Projects Dissertations, Theses, and Capstone Projects 6-2016 Increasing Accessibility for Map Readers with Acquired and Inherited Color Vision Deficiencies: A Re-Coloring Algorithm for Maps Gretchen M. Culp Graduate Center, City University of New York How does access to this work benefit ou?y Let us know! More information about this work at: https://academicworks.cuny.edu/gc_etds/1243 Discover additional works at: https://academicworks.cuny.edu This work is made publicly available by the City University of New York (CUNY). Contact: [email protected] Increasing Accessibility for Map Readers with Acquired and Inherited Color Vision Deficiencies: A Re-Coloring Algorithm for Maps by Gretchen Maria Culp A dissertation submitted to the Graduate Faculty in Earth and Environmental Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy, The City University of New York. 2016 ©2016 Gretchen Maria Culp All Rights Reserved ii This manuscript has been read and accepted for the Graduate Faculty in Earth and Envi- ronmental Sciences in satisfaction of the dissertation requirements for the degree of Doctor of Philosophy. Date Dr. Juliana Maantay, Lehman College Chair of Examining Committee Date Dr. Cindi Katz, The Graduate Center Executive Officer Supervisory Committee Dr. Allan Frei, Hunter College Dr. Andrew Maroko, Lehman College THE CITY UNIVERSITY OF NEW YORK iii Abstract Increasing Accessibility for Map Readers with Acquired and Inherited Color Vision Deficiencies: A Re-Coloring Algorithm for Maps by Gretchen Maria Culp Advisor: Dr. Juliana Maantay Approximately 8% of the male population suffer from an inherited form of color vision deficiency (CVD). Age, diabetes, macular degeneration, cataracts and glaucoma result in eye defects including an acquired form of CVD. Inherited CVD is marked by a difficulty in discerning red from green, while acquired CVD is marked by a difficulty in discerning blue from green. A recent review of the cartographic literature revealed a deficit in studies on accessible maps for readers with the acquired form of CVD. In addition, research on accessible maps for readers with the inherited form of CVD was restricted to the design or pre-publication stage. An approach is needed to render maps already in circulation accessible to an audience with CVD. The purpose of this research is to improve the accessibility of maps post-publication. Image re-coloring is a method of altering an image's color composition in such a way as to make it accessible to a color vision deficient audience. An innovative algorithm is presented that produces a re-colored map that can be perceived by individuals with red-green (inherited) CVD, blue-green CVD (acquired) and normal color vision alike. iv The algorithm was tested on a control group of participants with normal color vision and a case group of participants with impaired color vision through a series of matching, content and personal preference questions about six pairs of maps. Each map pair represented one of the following color schemes: balance, diverging, qualitative area, qualitative dot, sequential polychrome, and two variable. Each map pair is composed of two renditions: a map using a color palette that is potentially confusing to viewers with impaired color vision (original rendition) and a map where the original color palette has been re-colored by the algorithm (re-colored rendition). According to the results of a Wilcoxon signed-rank test, the performance of the case group improved when using the re-colored renditions compared to when using the original renditions while the performance of the control group was the same for both renditions. A Mann-Whitney rank sum test revealed that while the scores of the case group were lower than the control group when using the original renditions, they were the same when using the re-colored renditions. A binomial test revealed that subjects in the case group displayed a preference towards all the re-colored renditions while subjects in the control group displayed a preference to two of the six original renditions. v Acknowledgements I am indebted to my advisor Dr. Juliana Maantay and my committee members Dr. Allan Frei and Dr. Andrew Maroko. Without their insight and support, I could not have come this far. I am extremely grateful for the assistance I received from Dr. James Gordon, Dr. Israel Abramov, and Ms. Valerie Nunez of the Hunter College Laboratory of Visual Psychophysiology. My patient and learned colleague Dr. Sungwoo Lim was integral to my data analysis. This research would not have been possible without the efforts of my participants. The color blind subreddit community was immensely helpful and encouraging. I would like to thank Dr. Jeanine Meyer and Dr. Peter Ohring for teaching me how to program, a skill that changed my life. Finally, I would like to recognize the sacrifices made by my family over the past eight years. Their love and encouragement gave me the confidence to succeed. vi Preface All of the work presented henceforth was approved by the Herbert H. Lehman College (CUNY) HRPP Office [586203-2]. This dissertation is original, independent work by the author, G.M. Culp. All images included in this dissertation were produced by the author unless otherwise specified. A previous version of the re-coloring algorithm described in Chapter 3 has been published (Culp, 2012). I was the lead investigator, responsible for all major areas of concept formation, data collection and analysis, as well as manuscript composition. vii Table of Contents Abstract . iv Acknowledgements . vi Preface . vii Chapter 1. Introduction . 1 1.1. Research Question and Hypotheses . 3 1.2. Background . 4 1.2.1. The Eye . 4 1.2.2. Color Vision Deficiencies . 8 1.2.2.1. Inherited Color Vision Deficiencies . 13 1.2.2.2. Acquired Color Vision Deficiencies . 14 1.2.3. Color Vision Tests . 19 1.2.4. The Role of Color in the Visualization of Information . 24 Chapter 2. Literature Review . 26 2.1. Previous Cartographic Research on Color Vision . 26 2.2. Cartographic Color Scheme Design Software . 28 2.3. Related Image Re-Coloring Research . 29 2.3.1. Color Contrast Enhancing Algorithms . 30 2.3.2. Gamut Re-Mapping Algorithms . 33 viii 2.3.3. Daltonization Algorithms . 35 2.3.4. Grayscale Conversion Algorithms . 37 Chapter 3. Methodology . 39 3.1. OGA Re-Coloring Algorithm Development . 39 3.2. Map Production . 44 3.3. Color Scheme Selection . 45 3.4. Questionnaire Design . 49 3.5. Participant Selection . 49 3.6. Data Collection . 50 3.7. Data Analysis . 52 Chapter 4. Results . 56 4.1. Wilcoxon Signed Rank Tests . 56 4.1.1. Summary Statistics . 56 4.1.2. Significance Testing . 56 4.1.3. Effect Size . 57 4.1.3.1. Control Group Comparisons . 57 4.1.3.2. Case Group Comparisons . 57 4.2. Mann-Whitney Rank Sum Test . 63 4.2.1. Summary Statistics . 63 4.2.2. Significance Testing . 63 4.2.3. Effect Size . 64 4.2.3.1. Original Rendition Comparisons . 64 ix 4.2.3.2. Re-Colored Rendition Comparisons . 64 4.3. Binomial Analysis of Map Preference . 70 Chapter 5. Discussion . 71 5.1. Interpretation of Statistical Tests . 71 5.2. Additional Participant Details . 72 5.3. Features of the OGA Algorithm . 73 5.4. Limitations of the OGA Algorithm . 74 5.5. Conclusion . 75 Appendix A. Formulae . 76 A.1. Dichromatic Vision Simulation Equations . 76 A.2. Color space conversion from CIE 1931 RGB to CIE 1931 xyY . 80 A.3. Color space conversion from CIE 1931 XYZ to CIELAB . 81 A.4. Color space conversion from CIELAB to CIE 1931 XYZ . 82 A.5. Color space conversion from CIE 1931 xyY to CIE 1931 RGB . 83 A.6. Color space conversion from CIE 1931 RGB to oRGB . 84 A.7. Color space conversion from oRGB to CIE 1931 RGB . 84 A.8. CIEDE2000 Color Difference Equations . 85 A.9. Color space conversion from CIE 1931 RGB to HSV . 87 Appendix B. Maps . 88 Appendix C. Color Schemes . 101 Appendix D. Survey Questions . 109 x D.1. Background Questions . 109 D.2. Practice Map . 111 D.3. Map 1: Diverging, Rendition 1 . 112 D.4. Map 2: Diverging, Rendition 2 . 114 D.5. Map 3: Qualitative Dot, Rendition 1 . 116 D.6. Map 4: Qualitative Dot, Rendition 2 . 117 D.7. Map 5: Two Variable, Rendition 1 . 118 D.8. Map 6: Two Variable, Rendition 2 . 121 D.9. Map 7: Sequential Polychrome, Rendition 1 . 124 D.10. Map 8: Sequential Polychrome, Rendition 2 . 126 D.11. Map 9: Balance, Rendition 1 . 128 D.12. Map 10: Balance, Rendition 2 . 129 D.13. Map 11: Qualitative Area, Rendition 1 . 131 D.14. Map 12: Qualitative Area, Rendition 2 . 132 Bibliography . 143 xi List of Figures Figure 1.1. Diagram of the human eye. 6 Figure 1.2. Distribution of photorceptors in the human retina. 6 Figure 1.3. Foveal cone mosaic. 6 Figure 1.4. Two-stage model of human color vision. 7 Figure 1.5. CVD cone fundamentals by severity and type. 9 Figure 1.6. Simulation of dichromatic vision. 11 Figure 1.7. Gamut by color vision type. 12 Figure 1.8. Ishihara pseudoisochromatic plate. 22 Figure 1.9. Hardy-Rand-Rittler pseudoisochromatic plate. 22 Figure 1.10. Color vision panel tests. 23 Figure 1.11. Typical D-15 cap arrangements. 23 Figure 2.1. Color contrast enhancing algorithm. 32 Figure 2.2. Gamut re-mapping algorithm. 34 Figure 2.3. Daltonization algorithm. 36 Figure 2.4. Grayscale Algorithm. 38 Figure 3.1. Figure 1.6 map re-colored by the OGA algorithm. 41 Figure 3.2.